522 lines
32 KiB
Plaintext
522 lines
32 KiB
Plaintext
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/*******************************************************************************
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* Copyright (c) 2019 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author raver119@gmail.com
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// @author Yurii Shyrma (iuriish@yahoo.com)
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//
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#include "cudnnUtils.h"
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#include <ops/declarable/helpers/convolutions.h>
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namespace nd4j {
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namespace ops {
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namespace platforms {
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//////////////////////////////////////////////////////////////////////////
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static void conv2dCUDNN(const LaunchContext* context,
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const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output,
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const int kH, const int kW,
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const int sH, const int sW,
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const int pH, const int pW,
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const int dH, const int dW,
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const int paddingMode, const bool isNCHW) {
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int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
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int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
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auto handle = reinterpret_cast<cudnnHandle_t *>(context->getCuDnnHandle());
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cudnnStatus_t err = cudnnSetStream(*handle, *context->getCudaStream());
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if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: can't set stream for cuDNN", err);
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cudnnTensorFormat_t format = isNCHW ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC;
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// input descriptor
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cudnnTensorDescriptor_t x;
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cudnnCreateTensorDescriptor(&x);
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if(input->ews() == 1)
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err = cudnnSetTensor4dDescriptor(x, format, cudnnDataType(input->dataType()), bS, iC, iH, iW);
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else
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err = cudnnSetTensor4dDescriptorEx(x, cudnnDataType(input->dataType()), bS, iC, iH, iW, input->strideAt(0), input->strideAt(indIOioC), input->strideAt(indIiH), input->strideAt(indIiH + 1));
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if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for input failed", err);
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// weights descriptor
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cudnnFilterDescriptor_t w;
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cudnnCreateFilterDescriptor(&w);
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err = cudnnSetFilter4dDescriptor(w, cudnnDataType(weights->dataType()), CUDNN_TENSOR_NCHW, oC, iC, kH, kW);
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if(err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnSetFilter4dDescriptor failed", err);
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// output descriptor
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cudnnTensorDescriptor_t z;
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cudnnCreateTensorDescriptor(&z);
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if(output->ews() == 1)
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err = cudnnSetTensor4dDescriptor(z, format, cudnnDataType(output->dataType()), bS, oC, oH, oW);
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else
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err = cudnnSetTensor4dDescriptorEx(z, cudnnDataType(output->dataType()), bS, oC, oH, oW, output->strideAt(0), output->strideAt(indIOioC), output->strideAt(indOoH), output->strideAt(indOoH + 1));
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if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for output failed", err);
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// description of convolution
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cudnnConvolutionDescriptor_t conv;
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cudnnCreateConvolutionDescriptor(&conv);
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err = cudnnSetConvolution2dDescriptor(conv, pH, pW, sH, sW, dH, dW, CUDNN_CROSS_CORRELATION, cudnnDataType(output->dataType()));
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if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnSetConvolution2dDescriptor failed", err);
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// algorithm description
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cudnnConvolutionFwdAlgo_t algo;
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err = cudnnGetConvolutionForwardAlgorithm(*handle, x, w, conv, z, CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algo);
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if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnGetConvolutionForwardAlgorithm failed", err);
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// allocate auxiliary device memory, abbreviation ws means workspace
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size_t wsSize;
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err = cudnnGetConvolutionForwardWorkspaceSize(*handle, x, w, conv, z, algo, &wsSize);
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if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnGetConvolutionForwardWorkspaceSize failed", err);
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void* wsData;
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auto cudaErr = cudaMalloc(&wsData, wsSize);
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if (cudaErr != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudaMalloc for auxiliary workspace memory failed", cudaErr);
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// provide scaling parameters
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const float alpha32(1), beta32(0);
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const double alpha64(1), beta64(0);
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const void* alpha = output->sizeOfT() <= 4 ? reinterpret_cast<const void*>(&alpha32) : reinterpret_cast<const void*>(&alpha64);
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const void* beta = output->sizeOfT() <= 4 ? reinterpret_cast<const void*>(&beta32) : reinterpret_cast<const void*>(&beta64);
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NDArray::prepareSpecialUse({output}, {input, weights, bias});
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// run calculation
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err = cudnnConvolutionForward(*handle, alpha, x, input->getSpecialBuffer(), w, weights->getSpecialBuffer(), conv, algo, wsData, wsSize, beta, z, output->specialBuffer());
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if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnConvolutionForward failed", err);
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// add bias if it is present
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if (bias != nullptr) {
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cudnnTensorDescriptor_t b;
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cudnnCreateTensorDescriptor(&b);
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err = cudnnSetTensor4dDescriptor(b, format, cudnnDataType(bias->dataType()), 1, isNCHW ? bias->lengthOf() : 1, 1, isNCHW ? 1: bias->lengthOf());
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if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnSetTensor4dDescriptor for bias failed", err);
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err = cudnnAddTensor(*handle, alpha, b, bias->getSpecialBuffer(), alpha, z, output->specialBuffer());
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if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnAddTensor bias failed", err);
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}
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// cudaErr = cudaStreamSynchronize(*context->getCudaStream());
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// if (cudaErr != 0)
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// throw cuda_exception::build("conv2dCUDNN: cudaStreamSynchronize failed !", cudaErr);
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cudaErr = cudaFree(wsData);
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if (cudaErr != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudaFree for auxiliary workspace memory failed", cudaErr);
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NDArray::registerSpecialUse({output}, {input, weights, bias});
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}
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//////////////////////////////////////////////////////////////////////////
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static void conv2dBpCUDNN(const LaunchContext* context,
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const NDArray* input, const NDArray* weights, const NDArray* gradO,
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NDArray* gradI, NDArray* gradW, NDArray* gradB,
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const int kH, const int kW,
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const int sH, const int sW,
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const int pH, const int pW,
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const int dH, const int dW,
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const int paddingMode, const bool isNCHW) {
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int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
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int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
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auto handle = reinterpret_cast<cudnnHandle_t *>(context->getCuDnnHandle());
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cudnnStatus_t err = cudnnSetStream(*handle, *context->getCudaStream());
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if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: can't set stream for cuDNN", err);
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cudnnTensorFormat_t format = isNCHW ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC;
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// input descriptor
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cudnnTensorDescriptor_t x;
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cudnnCreateTensorDescriptor(&x);
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if(input->ews() == 1)
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err = cudnnSetTensor4dDescriptor(x, format, cudnnDataType(input->dataType()), bS, iC, iH, iW);
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else
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err = cudnnSetTensor4dDescriptorEx(x, cudnnDataType(input->dataType()), bS, iC, iH, iW, input->strideAt(0), input->strideAt(indIOioC), input->strideAt(indIiH), input->strideAt(indIiH + 1));
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if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for input failed", err);
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// gradO descriptor
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cudnnTensorDescriptor_t dz;
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cudnnCreateTensorDescriptor(&dz);
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if(gradO->ews() == 1)
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err = cudnnSetTensor4dDescriptor(dz, format, cudnnDataType(gradO->dataType()), bS, oC, oH, oW);
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else
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err = cudnnSetTensor4dDescriptorEx(dz, cudnnDataType(gradO->dataType()), bS, oC, oH, oW, gradO->strideAt(0), gradO->strideAt(indIOioC), gradO->strideAt(indOoH), gradO->strideAt(indOoH + 1));
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if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for gradO failed", err);
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// gradI descriptor
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cudnnTensorDescriptor_t dx;
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cudnnCreateTensorDescriptor(&dx);
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if(gradI->ews() == 1)
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err = cudnnSetTensor4dDescriptor(dx, format, cudnnDataType(gradI->dataType()), bS, iC, iH, iW);
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else
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err = cudnnSetTensor4dDescriptorEx(dx, cudnnDataType(gradI->dataType()), bS, iC, iH, iW, gradI->strideAt(0), gradI->strideAt(indIOioC), gradI->strideAt(indIiH), gradI->strideAt(indIiH + 1));
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if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for gradI failed", err);
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// gradW descriptor
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cudnnFilterDescriptor_t dw;
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cudnnCreateFilterDescriptor(&dw);
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err = cudnnSetFilter4dDescriptor(dw, cudnnDataType(gradW->dataType()), CUDNN_TENSOR_NCHW, oC, iC, kH, kW);
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if(err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnSetFilter4dDescriptor gradW failed", err);
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// description of convolution
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cudnnConvolutionDescriptor_t conv;
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cudnnCreateConvolutionDescriptor(&conv);
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err = cudnnSetConvolution2dDescriptor(conv, pH, pW, sH, sW, dH, dW, CUDNN_CROSS_CORRELATION, cudnnDataType(gradO->dataType()));
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if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnSetConvolution2dDescriptor failed", err);
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// gradW algorithm description
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cudnnConvolutionBwdFilterAlgo_t algoGradW;
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err = cudnnGetConvolutionBackwardFilterAlgorithm(*handle, x, dz, conv, dw, CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoGradW);
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if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnGetConvolutionBackwardFilterAlgorithm failed", err);
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// gradI algorithm description
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cudnnConvolutionBwdDataAlgo_t algoGradI;
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err = cudnnGetConvolutionBackwardDataAlgorithm(*handle, dw, dz, conv, x, CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoGradI);
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if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnGetConvolutionBackwardDataAlgorithm failed", err);
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// allocate auxiliary device memory for gradW calculation, abbreviation ws means workspace
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size_t wsGradWSize;
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err = cudnnGetConvolutionBackwardFilterWorkspaceSize(*handle, x, dz, conv, dw, algoGradW, &wsGradWSize);
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if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnGetConvolutionBackwardFilterWorkspaceSize failed", err);
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void* wsGradWData;
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auto cudaErr = cudaMalloc(&wsGradWData, wsGradWSize);
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if (cudaErr != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudaMalloc for auxiliary workspace memory wsGradWData failed", cudaErr);
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// allocate auxiliary device memory for gradI calculation, abbreviation ws means workspace
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size_t wsGradISize;
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err = cudnnGetConvolutionBackwardDataWorkspaceSize(*handle, dw, dz, conv, dx, algoGradI, &wsGradISize);
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if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnGetConvolutionBackwardDataWorkspaceSize failed", err);
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void* wsGradIData;
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cudaErr = cudaMalloc(&wsGradIData, wsGradISize);
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if (cudaErr != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudaMalloc for auxiliary workspace memory wsGradIData failed", cudaErr);
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// provide scaling parameters
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const float alpha32(1), beta32(0);
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const double alpha64(1), beta64(0);
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const void* alpha = gradO->sizeOfT() <= 4 ? reinterpret_cast<const void*>(&alpha32) : reinterpret_cast<const void*>(&alpha64);
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const void* beta = gradO->sizeOfT() <= 4 ? reinterpret_cast<const void*>(&beta32) : reinterpret_cast<const void*>(&beta64);
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NDArray::prepareSpecialUse({gradI, gradW, gradB}, {input, weights, gradO});
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// run calculation for gradB (if not nullptr)
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if(gradB != nullptr) {
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cudnnTensorDescriptor_t db;
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cudnnCreateTensorDescriptor(&db);
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err = cudnnSetTensor4dDescriptor(db, format, cudnnDataType(gradB->dataType()), 1, isNCHW ? gradB->lengthOf() : 1, 1, isNCHW ? 1: gradB->lengthOf());
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if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnSetTensor4dDescriptor for gradB failed", err);
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err = cudnnConvolutionBackwardBias(*handle, alpha, dz, gradO->getSpecialBuffer(), beta, db, gradB->getSpecialBuffer());
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if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnConvolutionBackwardBias failed", err);
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}
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// run calculation for gradW
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err = cudnnConvolutionBackwardFilter(*handle, alpha, x, input->getSpecialBuffer(), dz, gradO->getSpecialBuffer(), conv, algoGradW, wsGradWData, wsGradWSize, beta, dw, gradW->getSpecialBuffer());
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if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnConvolutionBackwardFilter failed", err);
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// run calculation for gradI
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err = cudnnConvolutionBackwardData(*handle, alpha, dw, weights->getSpecialBuffer(), dz, gradO->getSpecialBuffer(), conv, algoGradI, wsGradIData, wsGradISize, beta, dx, gradI->getSpecialBuffer());
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if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnConvolutionBackwardData failed", err);
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// cudaErr = cudaStreamSynchronize(*context->getCudaStream());
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// if (cudaErr != 0)
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// throw cuda_exception::build("conv2dBpCUDNN: cudaStreamSynchronize failed !", cudaErr);
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cudaErr = cudaFree(wsGradWData);
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if (cudaErr != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudaFree for auxiliary workspace memory wsGradWData failed", cudaErr);
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cudaErr = cudaFree(wsGradIData);
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if (cudaErr != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudaFree for auxiliary workspace memory wsGradIData failed", cudaErr);
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NDArray::registerSpecialUse({gradI, gradW, gradB}, {input, weights, gradO});
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}
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//////////////////////////////////////////////////////////////////////////
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PLATFORM_IMPL(conv2d, ENGINE_CUDA) {
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auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always
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auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto output = OUTPUT_VARIABLE(0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
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|
|
|
||
|
|
int sH = INT_ARG(2); // strides height
|
||
|
|
int sW = INT_ARG(3); // strides width
|
||
|
|
int pH = INT_ARG(4); // paddings height
|
||
|
|
int pW = INT_ARG(5); // paddings width
|
||
|
|
int dH = INT_ARG(6); // dilations height
|
||
|
|
int dW = INT_ARG(7); // dilations width
|
||
|
|
int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
|
||
|
|
bool isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
|
||
|
|
|
||
|
|
int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0)); // filter(kernel) height
|
||
|
|
int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1)); // filter(kernel) width
|
||
|
|
|
||
|
|
REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM CONV2D CUDNN OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
|
||
|
|
REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM CONV2D CUDNN OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf());
|
||
|
|
|
||
|
|
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
|
||
|
|
int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
|
||
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
|
||
|
|
|
||
|
|
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
|
||
|
|
|
||
|
|
std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, iC, oC};
|
||
|
|
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM CONV2D CUDNN OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
|
||
|
|
if (bias) {
|
||
|
|
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV2D CUDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
|
||
|
|
REQUIRE_TRUE((bias->rankOf() == 1 && bias->strideAt(0) == 1) || (bias->rankOf() == 2 && bias->sizeAt(0) == 1 && bias->strideAt(1) == 1) || (bias->rankOf() == 2 && bias->sizeAt(1) == 1 && bias->strideAt(0) == 1), 0, "CUSTOM CONV2D CUDNN OP: bias array should be contiguous in memory !");
|
||
|
|
}
|
||
|
|
|
||
|
|
NDArray* newWeights = new NDArray(weights->ordering(), {oC, iC, kH, kW}, weights->dataType(), weights->getContext()); // cudnn support only two formats {oC,iC,kH,kW} and {oC,kH,kW,iC}
|
||
|
|
newWeights->assign(weights->permute({3,2,0,1})); // permute weights (kH, kW, iC, oC --> oC, iC, kH, kW)
|
||
|
|
|
||
|
|
NDArray* newInput = input;
|
||
|
|
NDArray* newGradI = nullptr;
|
||
|
|
if(paddingMode == 1) // in same paddingMode cudnn doesn't support asymmetric left/right top/bottopm paddings
|
||
|
|
checkConv2dCUDNNPadAsymmetric(newInput, newGradI, iH, iW, oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, isNCHW);
|
||
|
|
|
||
|
|
conv2dCUDNN(block.launchContext(), newInput, newWeights, bias, output, kH,kW,sH,sW,pH,pW,dH,dW, paddingMode, isNCHW);
|
||
|
|
|
||
|
|
if(newInput != input)
|
||
|
|
delete newInput;
|
||
|
|
|
||
|
|
delete newWeights;
|
||
|
|
|
||
|
|
return Status::OK();
|
||
|
|
}
|
||
|
|
|
||
|
|
//////////////////////////////////////////////////////////////////////////
|
||
|
|
PLATFORM_CHECK(conv2d, ENGINE_CUDA) {
|
||
|
|
|
||
|
|
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
|
||
|
|
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always
|
||
|
|
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
||
|
|
|
||
|
|
const int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME, 2-CAUSAL
|
||
|
|
|
||
|
|
const bool badInputType = input->dataType() != DataType::DOUBLE && input->dataType() != DataType::FLOAT32 && input->dataType() != DataType::HALF;
|
||
|
|
const bool badWeightsType = weights->dataType() != DataType::DOUBLE && weights->dataType() != DataType::FLOAT32 && weights->dataType() != DataType::HALF;
|
||
|
|
const bool badBiasType = bias == nullptr ? false : (bias->dataType() != DataType::DOUBLE && bias->dataType() != DataType::FLOAT32 && bias->dataType() != DataType::HALF);
|
||
|
|
|
||
|
|
return paddingMode != 2 && !badInputType && !badWeightsType && !badBiasType;
|
||
|
|
}
|
||
|
|
|
||
|
|
//////////////////////////////////////////////////////////////////////////
|
||
|
|
PLATFORM_IMPL(conv2d_bp, ENGINE_CUDA) {
|
||
|
|
|
||
|
|
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
|
||
|
|
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always
|
||
|
|
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
||
|
|
auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
|
||
|
|
|
||
|
|
auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
|
||
|
|
auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, iC, oC] always
|
||
|
|
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
|
||
|
|
|
||
|
|
int kH = INT_ARG(0); // filter(kernel) height
|
||
|
|
int kW = INT_ARG(1); // filter(kernel) width
|
||
|
|
int sH = INT_ARG(2); // strides height
|
||
|
|
int sW = INT_ARG(3); // strides width
|
||
|
|
int pH = INT_ARG(4); // paddings height
|
||
|
|
int pW = INT_ARG(5); // paddings width
|
||
|
|
int dH = INT_ARG(6); // dilations height
|
||
|
|
int dW = INT_ARG(7); // dilations width
|
||
|
|
int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
|
||
|
|
int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
|
||
|
|
|
||
|
|
REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM CONV2D_BP CUDNN OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
|
||
|
|
REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM CONV2D_BP CUDNN OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf());
|
||
|
|
REQUIRE_TRUE(gradO->rankOf() == 4, 0, "CUSTOM CONV2D_BP CUDNN OP: rank of output's gradients (next epsilon) array must be equal to 4, but got %i instead !", gradO->rankOf());
|
||
|
|
|
||
|
|
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
|
||
|
|
int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
|
||
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
|
||
|
|
|
||
|
|
int trueoH, trueoW; // true output height, width
|
||
|
|
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, paddingMode);
|
||
|
|
|
||
|
|
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
|
||
|
|
|
||
|
|
std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1});
|
||
|
|
std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, iC, oC};
|
||
|
|
REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CUSTOM CONV2D_BP CUDNN OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
|
||
|
|
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM CONV2D_BP CUDNN OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
|
||
|
|
if(bias)
|
||
|
|
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV2D_BP CUDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
|
||
|
|
|
||
|
|
NDArray* newGradW = new NDArray(gradW->ordering(), {oC, iC, kH, kW}, gradW->dataType(), gradW->getContext()); // cudnn support only two formats for weights {oC,iC,kH,kW} and {oC,kH,kW,iC}
|
||
|
|
NDArray* newWeights = new NDArray(weights->ordering(), {oC, iC, kH, kW}, weights->dataType(), weights->getContext());
|
||
|
|
|
||
|
|
newWeights->assign(weights->permute({3,2,0,1})); // permute weights (kH, kW, iC, oC --> oC, iC, kH, kW)
|
||
|
|
|
||
|
|
NDArray* newInput = input;
|
||
|
|
NDArray* newGradI = gradI;
|
||
|
|
if(paddingMode == 1) // in same paddingMode cudnn doesn't support asymmetric left/right top/bottopm paddings
|
||
|
|
checkConv2dCUDNNPadAsymmetric(newInput, newGradI, iH, iW, oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, isNCHW);
|
||
|
|
|
||
|
|
conv2dBpCUDNN(block.launchContext(), newInput, newWeights, gradO, newGradI, newGradW, gradB, kH,kW,sH,sW,pH,pW,dH,dW,paddingMode,isNCHW);
|
||
|
|
|
||
|
|
newGradW->permutei({2,3,1,0}); // [oC, iC, kH, kW] -> [kH, kW, iC, oC]
|
||
|
|
gradW->assign(newGradW);
|
||
|
|
|
||
|
|
if(newInput != input) {
|
||
|
|
|
||
|
|
if(isNCHW)
|
||
|
|
gradI->assign((*newGradI)({0,0, 0,0, 0,gradI->sizeAt(2), 0,gradI->sizeAt(3)}));
|
||
|
|
else
|
||
|
|
gradI->assign((*newGradI)({0,0, 0,gradI->sizeAt(1), 0,gradI->sizeAt(2), 0,0}));
|
||
|
|
|
||
|
|
delete newInput;
|
||
|
|
delete newGradI;
|
||
|
|
}
|
||
|
|
|
||
|
|
delete newWeights;
|
||
|
|
delete newGradW;
|
||
|
|
|
||
|
|
return Status::OK();
|
||
|
|
}
|
||
|
|
|
||
|
|
PLATFORM_CHECK(conv2d_bp, ENGINE_CUDA) {
|
||
|
|
|
||
|
|
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
|
||
|
|
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always
|
||
|
|
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
||
|
|
auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
|
||
|
|
|
||
|
|
const int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME, 2-CAUSAL
|
||
|
|
const int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
|
||
|
|
|
||
|
|
const bool badInputType = input->dataType() != DataType::DOUBLE && input->dataType() != DataType::FLOAT32 && input->dataType() != DataType::HALF;
|
||
|
|
const bool badWeightsType = weights->dataType() != DataType::DOUBLE && weights->dataType() != DataType::FLOAT32 && weights->dataType() != DataType::HALF;
|
||
|
|
const bool badGradOType = gradO->dataType() != DataType::DOUBLE && gradO->dataType() != DataType::FLOAT32 && gradO->dataType() != DataType::HALF;
|
||
|
|
const bool badBiasType = bias == nullptr ? false : (bias->dataType() != DataType::DOUBLE && bias->dataType() != DataType::FLOAT32 && bias->dataType() != DataType::HALF);
|
||
|
|
|
||
|
|
return isNCHW && paddingMode != 2 && !badInputType && !badWeightsType && !badGradOType && !badBiasType;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
// PLATFORM_IMPL(conv2d, ENGINE_CUDA) {
|
||
|
|
|
||
|
|
// auto handle = reinterpret_cast<cudnnHandle_t *>(block.launchContext()->getCuDnnHandle());
|
||
|
|
// auto res = cudnnSetStream(*handle, *block.launchContext()->getCudaStream());
|
||
|
|
// if (res != 0)
|
||
|
|
// throw nd4j::cuda_exception::build("Can't set stream for cuDNN", res);
|
||
|
|
|
||
|
|
// auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
|
||
|
|
// auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always
|
||
|
|
// auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
||
|
|
|
||
|
|
// auto output = OUTPUT_VARIABLE(0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
|
||
|
|
|
||
|
|
// NDArray::prepareSpecialUse({output}, {input, weights, bias});
|
||
|
|
|
||
|
|
// int sH = INT_ARG(2); // strides height
|
||
|
|
// int sW = INT_ARG(3); // strides width
|
||
|
|
// int pH = INT_ARG(4); // paddings height
|
||
|
|
// int pW = INT_ARG(5); // paddings width
|
||
|
|
// int dH = INT_ARG(6); // dilations height
|
||
|
|
// int dW = INT_ARG(7); // dilations width
|
||
|
|
// int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
|
||
|
|
// bool isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
|
||
|
|
|
||
|
|
// int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0)); // filter(kernel) height
|
||
|
|
// int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1)); // filter(kernel) width
|
||
|
|
|
||
|
|
// int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
|
||
|
|
// int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
|
||
|
|
// ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
|
||
|
|
// ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, isSameMode);
|
||
|
|
|
||
|
|
// auto dtype = cudnnDataType(input->dataType());
|
||
|
|
|
||
|
|
|
||
|
|
// cudnnTensorDescriptor_t src;
|
||
|
|
// cudnnCreateTensorDescriptor(&src);
|
||
|
|
// res = cudnnSetTensor4dDescriptorEx(src, dtype, input->sizeAt(0), input->sizeAt(1), input->sizeAt(2), input->sizeAt(3), input->strideAt(0), input->strideAt(1), input->strideAt(2), input->strideAt(3));
|
||
|
|
// if (res != 0)
|
||
|
|
// throw nd4j::cuda_exception::build("cudnnSetTensor4dDescriptorEx src failed", res);
|
||
|
|
|
||
|
|
// // TODO: we definitely want NHWC here as well
|
||
|
|
// cudnnFilterDescriptor_t wght;
|
||
|
|
// cudnnCreateFilterDescriptor(&wght);
|
||
|
|
// res = cudnnSetFilter4dDescriptor(wght, dtype, CUDNN_TENSOR_NCHW, oC, iC, kH, kW);
|
||
|
|
// if (res != 0)
|
||
|
|
// throw nd4j::cuda_exception::build("cudnnSetFilter4dDescriptor failed", res);
|
||
|
|
|
||
|
|
// cudnnConvolutionDescriptor_t cdc;
|
||
|
|
// cudnnCreateConvolutionDescriptor(&cdc);
|
||
|
|
// res = cudnnSetConvolution2dDescriptor(cdc, pH, pW, sH, sW, dH, dW, CUDNN_CROSS_CORRELATION, dtype);
|
||
|
|
// if (res != 0)
|
||
|
|
// throw nd4j::cuda_exception::build("cudnnSetConvolution2dDescriptor failed", res);
|
||
|
|
|
||
|
|
// cudnnTensorDescriptor_t dst;
|
||
|
|
// cudnnCreateTensorDescriptor(&dst);
|
||
|
|
// res = cudnnSetTensor4dDescriptorEx(dst, dtype, output->sizeAt(0), output->sizeAt(1), output->sizeAt(2), output->sizeAt(3), output->strideAt(0), output->strideAt(1), output->strideAt(2), output->strideAt(3));
|
||
|
|
// if (res != 0)
|
||
|
|
// throw nd4j::cuda_exception::build("cudnnSetTensor4dDescriptorEx dst failed", res);
|
||
|
|
|
||
|
|
// // TODO: workspace algorithms are supposed to be faster, so we should use it here if we have enough memory
|
||
|
|
// cudnnConvolutionFwdAlgo_t algo;
|
||
|
|
// res = cudnnGetConvolutionForwardAlgorithm(*handle, src, wght, cdc, dst, CUDNN_CONVOLUTION_FWD_NO_WORKSPACE, 0, &algo);
|
||
|
|
// if (res != 0)
|
||
|
|
// throw nd4j::cuda_exception::build("cudnnGetConvolutionForwardAlgorithm failed", res);
|
||
|
|
|
||
|
|
// // TODO: should be float if dtype is half/float, and double otherwise
|
||
|
|
// float alpha = 1.0f;
|
||
|
|
// float beta = 0.0f;
|
||
|
|
// res = cudnnConvolutionForward(*handle, &alpha, src, input->specialBuffer(), wght, weights->specialBuffer(), cdc, algo, nullptr, 0, &beta, dst, output->specialBuffer());
|
||
|
|
// if (res != 0)
|
||
|
|
// throw nd4j::cuda_exception::build("cudnnConvolutionForward failed", res);
|
||
|
|
|
||
|
|
|
||
|
|
// if (bias != nullptr) {
|
||
|
|
// cudnnTensorDescriptor_t bs;
|
||
|
|
// cudnnCreateTensorDescriptor(&bs);
|
||
|
|
// if (isNCHW) {
|
||
|
|
// res = cudnnSetTensor4dDescriptor(bs, CUDNN_TENSOR_NCHW, dtype, 1, bias->lengthOf(), 1, 1);
|
||
|
|
// if (res != 0)
|
||
|
|
// throw nd4j::cuda_exception::build("cudnnSetTensor4dDescriptorEx bias NHWC failed", res);
|
||
|
|
// } else {
|
||
|
|
// res = cudnnSetTensor4dDescriptor(bs, CUDNN_TENSOR_NHWC, dtype, 1, 1, 1, bias->lengthOf());
|
||
|
|
// if (res != 0)
|
||
|
|
// throw nd4j::cuda_exception::build("cudnnSetTensor4dDescriptorEx bias NHWC failed", res);
|
||
|
|
// }
|
||
|
|
|
||
|
|
// res = cudnnAddTensor(*handle, &alpha, bs, bias->specialBuffer(), &alpha, dst, output->specialBuffer());
|
||
|
|
// if (res != 0)
|
||
|
|
// throw nd4j::cuda_exception::build("cudnnAddTensor failed", res);
|
||
|
|
// }
|
||
|
|
|
||
|
|
|
||
|
|
// NDArray::registerSpecialUse({output}, {input, weights, bias});
|
||
|
|
|
||
|
|
// return Status::OK();
|
||
|
|
// }
|
||
|
|
|
||
|
|
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|